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Google open source AI which searches tables to answer questions in natural language

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Today, Google has opened a machine learning model that can point to answers to questions in natural language (for example, which wrestler has had the most kingdoms?) In spreadsheets and databases. The creators of models claim that it is able to find even answers distributed over several cells or which could require the aggregation of several cells.

Much of the world’s information is stored in tabular form, says Google Researchs Thomas Mller in a blog post, such as global financial statistics and sports scores. But these tables often lack an intuitive way to browse them, a problem that the Googles AI model aims to solve.

To deal with questions such as Average time as champion of the 2 best wrestlers ?, The model jointly codes the question as well as the content of the table line by line. It is based on a BERT architecture based on Transformer, an architecture that is both bidirectional (allowing it to access the content of past and future directions) and unsupervised (which means that it can ingest data that is neither classified or labeled) extended as well as numerical representations called incorporations to code the structure table.

A key addition was the incorporations used to code the structured entry, according to Mller. The learnings incorporated for the column index, the row index and a special classification index tell the model the order of the elements in the numeric columns.

Above: a table and questions with the expected answers. Answers can be selected (# 1, # 4) or calculated (# 2, # 3).

Image Credit: Google

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For each cell in the table, the model generates a score indicating the probability that the cell is part of the response. In addition, it generates an operation (for example, AVERAGE, SUM or COUNT) indicating which operation (if any) should be applied to produce the final response.

To pre-train the model, the researchers extracted 6.2 million table-text pairs from English Wikipedia, which served as a training dataset. During pre-training, with relatively high accuracy, the model learned to restore words in tables and text that had been deleted 71.4% of the items were restored correctly for tables that were invisible during training.

After pre-training, Mller and his team refined the model via weak supervision, using limited sources to provide signals to label training data. They report that the best model has surpassed the state of the art for the sequential response dataset, a benchmark created by Microsoft to explore the task of answering questions on tables, by 12 points. He also beat the previous top model on Stanfords WikiTableQuestions, which contains questions and tables from Wikipedia.

The weak supervision scenario is beneficial because it allows non-experts to provide the data necessary to train the model and takes less time than strong supervision, said Mller.

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